Car Model Detection using ResNet50. This is a Python project that uses transfer learning with the ResNet50 model to detect the brand of cars. The frontend of the project is a web-based application built on Flask .
The model uses the ResNet50 architecture which is a deep learning neural network that has been pre-trained on a large dataset. The ResNet50 model is fine-tuned on a dataset of car images to detect the brand of the car.
To run the application, follow the steps below:
- Clone the repository to your local machine:
git clone https://github.com/maririn312/resnet_car_model_detection- Install the necessary dependencies in your local pc and google colab:
pip install -r requirements.txt
!pip install flask
!pip install tensorflow
3. Run the Flask application:
```bash
python car-detection-flask.py- Open a web browser and navigate to
http://localhost:8080to use the application.
NOTE: I would like to inform you that the model used in the project is not available on the GitHub repository due to its size exceeding the platform's limit. Therefore, I kindly request that you reach out to the appropriate administrator or authority to request access to the model.
The current version of the model is not always accurate and may need to be fine-tuned to improve its performance. To do this, you can modify the hyperparameters of the model in the car-detection.ipynb file and retrain the model on a larger dataset.
Car Model Detection was created by Amane Marin. This project is based on the ResNet50 model implemented in Keras. The car dataset used for training and testing the model was local.